Lung cancer is the most common cause of cancer death in both men and women in the United States. Reliable generalizable methods to detect early cancer within the bronchoscopically visible (central) airways are urgently needed. This development needs to occur concurrently with other efforts already underway to identify early peripheral lung cancers. The characteristics of early cancer within the human airway are thickening of the airway mucosa, and a change of color of the mucosal surface. At an earlier stage there will likely be protein and molecular changes, but these are so far poorly described. However, the expectation is that within several years there will be fluorescent markers that could be used to identify cancer changes within the human airway at this early stage. Recently there have been major advances in human imaging hardware and software. The first true color CCD bronchoscopes have been commercially introduced within the last 12 months. The digital output provides better color and spatial resolution. CT scanning has also benefited in speed and resolution through the introduction of multislice spiral scanners. One problem with these technologies is that the richness of the digital information further compounds the substantial human observer error rate in reporting on abnormalities. The purpose of this phased innovation award application is to develop digital analytic color detection and analysis of the human airway mucosa with images from the CCD color bronchoscope; to develop digital analysis and display of the airway mucosa from high resolution/high speed volumetric CT scan data; and to integrate these two complimentary modalities into a single generalizable early cancer detection tool. At the completion of the project we will have developed this integrated automated and analytic tool, and evaluated this in a human population at high risk for lung cancer. We will be able to define the characteristics of the normal human airway, and the human airway in smoking subjects at high risk of lung cancer. We will know the positive and negative predicted values for airway mucosal lesions determined by CT scan (thickening and abnormal topography), and by abnormal airway color, both separately and collectively against a pathologic gold standard. In the future, we expect this technology to be easily used and generally available for undertaking effective screening for lung cancer within the bronchoscopically visible airways. Such technology will be useful for the evaluation of molecular probes as they are developed, and in promoting image guided airway cancer treatment. Lung cancer is the most common cause of cancer death in both men and women in the United States. Reliable generalizable methods to detect early cancer within the bronchoscopically visible (central) airways are urgently needed. This development needs to occur concurrently with other efforts already underway to identify early peripheral lung cancers. The characteristics of early cancer within the human airway are thickening of the airway mucosa, and a change of color of the mucosal surface. At an earlier stage there will likely be protein and molecular changes, but these are so far poorly described. However, the expectation is that within several years there will be fluorescent markers that could be used to identify cancer changes within the human airway at this early stage. Recently there have been major advances in human imaging hardware and software. The first true color CCD bronchoscopes have been commercially introduced within the last 12 months. The digital output provides better color and spatial resolution. CT scanning has also benefited in speed and resolution through the introduction of multislice spiral scanners. One problem with these technologies is that the richness of the digital information further compounds the substantial human observer error rate in reporting on abnormalities. The purpose of this phased innovation award application is to develop digital analytic color detection and analysis of the human airway mucosa with images from the CCD color bronchoscope; to develop digital analysis and display of the airway mucosa from high resolution/high speed volumetric CT scan data; and to integrate these two complimentary modalities into a single generalizable early cancer detection tool. At the completion of the project we will have developed this integrated automated and analytic tool, and evaluated this in a human population at high risk for lung cancer. We will be able to define the characteristics of the normal human airway, and the human airway in smoking subjects at high risk of lung cancer. We will know the positive and negative predicted values for airway mucosal lesions determined by CT scan (thickening and abnormal topography), and by abnormal airway color, both separately and collectively against a pathologic gold standard. In the future, we expect this technology to be easily used and generally available for undertaking effective screening for lung cancer within the bronchoscopically visible airways. Such technology will be useful for the evaluation of molecular probes as they are developed, and in promoting image guided airway cancer treatment.